Multimedia Image Retrieval System by Combining CNN With Handcraft Features in Three Different Similarity Measures

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Multimedia Image Retrieval System by Combining CNN With Handcraft Features in Three Different Similarity Measures

机译：在三种不同的相似度量中将CNN与手工艺特征相结合的多媒体图像检索系统

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The authors propose WNAHVF, a combined weighted and normalized AlexNet with handcrafted visual features for extracting features from images and using those vectors for image retrieval and classification. The authors test the WNAHVF method on two general datasets, Corel-1k and Corel-10k, and one medical dataset. The outcomes demonstrate combining Bag of Features and Local Neighbor patterns with AlexNet enhances the accuracy and gives better results in general and medical image datasets in retrieval and classification problems. This algorithm gives results that are superior to existing strategies.

机译：作者提出了WNAHVF，这是一种加权的和归一化的AlexNet，具有手工制作的视觉特征，用于从图像中提取特征并将这些向量用于图像检索和分类。作者在两个通用数据集Corel-1k和Corel-10k以及一个医学数据集上测试了WNAHVF方法。结果表明，将功能袋和局部邻域模式与AlexNet结合使用可提高准确性，并在检索和分类问题中为常规和医学图像数据集提供更好的结果。该算法给出的结果优于现有策略。

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《International journal of computer vision and iImage processing》 |2020年第1期|1-23|共23页
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CSE Department JNTUH Collage of Engineering;

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正文语种 eng
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关键词
Bag of Features; Content-Based Image Retrieval; Convolutional Neural Network; Ensemble; K-Nearest Neighbor; Linear Discriminant; Local Neighbor Pattern; Medical Image Classification; Principal Component Analysis; Support Vector Machine;

机译：功能包;基于内容的图像检索;卷积神经网络合奏;K最近邻居;线性判别;本地邻居模式;医学图像分类;主成分分析;支持向量机;

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Multimedia Image Retrieval System by Combining CNN With Handcraft Features in Three Different Similarity Measures

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